article in English - Journal of Agribusiness and Rural Development

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DOI: 10.17306/JARD.2016.71
Journal of Agribusiness and Rural Development
3(41) 2016, 433–443
pISSN 1899-5241
eISSN 1899-5772
SUSTAINABLE DEVELOPMENT OF THE FARMS
IN POLAND
Jadwiga Zaród
Zachodniopomorski Uniwersytet Technologiczny w Szczecinie
Abstract. Based on statistical data of the Central Statistical
Office regarding Polish farms two linear-dynamic multicriteria optimization models have been created. The first model
concerned plant production, the other plant and animal production. In both models, objective functions maximized agricultural income and production, and minimized loss of organic soil matter. Balancing these three objectives is the essence
of a farm’s sustainable development. The models have been
solved with goal programming. The optimal solution yielded
a production structure allowing for the highest quality of production, under given conditions of agricultural income, with
no degradation of the natural environment. The goal of the following article is to confirm whether it is possible to simultaneously realize the production, economic and ecological goals
of Polish farms over the course of four years.
Key words: sustainable development, agricultural production, agricultural profit, soil organic matter, multicriteria optimization model
INTRODUCTION
The principles of sustainable development apply to all
sectors of economy and therefore agriculture. Sustainable development of farms includes predominantly: assurance of continuous soil fertility, abiding to the rules
of proper agricultural technology and animal husbandry,
usage of exclusively necessary chemical plant protection products, limiting the use of mineral fertilizers,
good vegetation soil coverage, reasonable furnishing of

farms in terms of mechanization and income to ensure
a decent standard of living for farmers.
The listed rules are aimed at realization of three
goals: economic and ecologic production, as well as
maintenance of the balance between them. Production
task involves manufacture, in appropriate amounts, of
agricultural products of quality required by the consumers or the processing industry. Economic actions are focused on the development of agricultural income comparable to salaries in other national economy sectors,
and allowing for farm modernization and development.
Ecologic goals aim at guaranteeing agroecosystem stability and natural environment’s degradation prevention.
The goal of the following article is check whether
it is possible to simultaneously realize the production,
as well as economic and ecologic goals of Polish farms
for four years. Such a research will make it possible to
make linear-dynamic multicriteria optimization models.
Many authors from around the world have already
dealt with agricultural production optimization. Among
others, Wesonga (2007) based on farm optimal models’ solution, suggested the structure of production,
that should significantly decrease poverty and hunger
in African countries. Manos et al. (2013) have studied agricultural production sustainable development in
the Tasalii (Greece) region using optimization models.
Sha-sha et al. (2013) optimized agricultural production
structure in uncertain conditions in Dancheny County
(China). Rodríguez et al. (2009) have used optimization
models for pig breeding planning.
dr Jadwiga Zaród, Katedra Zastosowań Matematyki w Ekonomii, Zachodniopomorski Uniwersytet Technologiczny w Szczecinie, ul. K. Janickiego 31, 71-270 Szczecin, Poland, e-mail: [email protected]
© Copyright by Wydawnictwo Uniwersytetu Przyrodniczego w Poznaniu
Zaród, J. (2016). Sustainable development of the farms in Poland. J. Agribus. Rural Dev., 3(41), 433–443. DOI: 10.17306/JARD.2016.71
As a result of the solutions of the models presented
in this work, a production structure will be formed that
will not degrade the environment while yielding the
highest possible, under given conditions agricultural,
income and assure a high standard of production.
RESEARCH METHOD
The main research method of the paper are linear-dynamic optimization models with three goal criteria. The
models describe a typical Polish farm, and their goal
functions relate to agricultural income, production volume and the amount of organic substances in the soil.
The mathematical model of such a goal adapted to
agricultural needs takes the form of (Krawiec, 1991):
ax(t) ≤ b(t) limiting conditions
(1)
x(t + 1) ≤ x(t)+ ft [x(t), u(t)] dynamics equations (2)
[g(t + 1)]Tu(t) ≥ [h(t + 1)]Txz(t + 1)] feed balance (3)
(in livestock production model)
F = max {F1, F2, F3} control criterion
(4)
x(t) ≥ 0, u(t) ≥ 0 boundary conditions
(5)
where:
t – states (consecutive years of farming), t = 0, 1, 2,…, k
a – technical and economic parameters’ vector
b(t) – subsequent states’ limits vector
x(t) – state vector
u(t) – control vector
g(t + 1) – fodder plant unit efficiency (yield) vector
h(t + 1) – annual individual demand for feed and plant
materials’ vector
xz(t + 1) – livestock state vector.
It should be assumed that the initial system state in
the t = 0 moment is known and describes the plant acreage and livestock state in the moment preceding the first
year of the research.
Vector of the x(t) takes the form:
x(t) = [xt(t), xp(t), xr(t)] = [x1(t),..., xn(t)]
(6)
where:
xt(t) – commodity operations’ state vector (it describes
the acreage of forage plants grown in the year t and
animal classes and species that yield commodity
production in the year t, like milk, meat)
xp(t) – subsistence operations’ state vector (it describes
the acreage of forage plants grown in the year t and
434
animal classes and species that do not yield commodity production)
xr(t) – other operations’ state vector, e.g. purchases of
production materials, feeds.
Control vector u(t) = uij(t) presents the flows inside
the farm or between the farm and its surroundings. This
vector’s components describe acreages of subsequent
plants, livestock class change, livestock sale or purchase, during the farms transition from state t to t+l. The
i, j¸ indices determine the order of succession, e.g. after
a plant i, plant j will be grown, or an animal of i class
will pass into j class.
Dynamics equations for plant production take the
form of:
xi t 1
¦u
pi
(7)
t p
where:
xi(t + 1) – acreage of i-th arable crop in the year t + 1
upi(t) – acreage of various forecrops p after which i-th is
grown in the year t + 1.
The dynamics equations’ form for livestock production
is as follows:
xi(t+1) = xi(t) – uis(t) + uiz(t) + uji(t)
(8)
where: xi(t + 1) – i-th species’ livestock state in the year
t+l
xi(t) – i-th species’ livestock state in the preceeding year
uis(t) – i-th species’ livestock sales in the t year
uiz(t) – i-th species’ livestock purchase in the t year
uji(t) – i-th species’ livestock quantity from own livestock, reclassing.
To sum up, it should be said that dynamics and constraints of linear equations transition of the farm from
state t to state t + 1, meaning from the previous to the
next research year. They incorporate: the farm’s state in
year t, control that could be utilized while transitioning from state t to state t+1, and limits of agricultural
production.
The F1 goal criterion regards gross agricultural income and is expressed with equation:
F1
T
T
¦[m(t ) u (t ) w(t 1) x(t 1)] o max
(9)
t
where: m(t), w(t + 1) – individual income vector for
variables control and state denoting commodity activity
or individual outlays incurred by farms involved in noncommodity activity.
F2 is a control criterion maximizing production volume, it takes the form of:
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Zaród, J. (2016). Sustainable development of the farms in Poland. J. Agribus. Rural Dev., 3(41), 433–443. DOI: 10.17306/JARD.2016.71
F2
T
T
¦[g (t ) u (t ) k (t 1) x(t 1)] o max
(10)
t
where: g(t), k(t + 1) – individual efficiency vector of
variable control and state in subsequent years;
The F3 function maximizes the soil organic substance’s amount:
F3
T
T
¦ [o (t ) u (t ) p (t 1) x(t 1)] o max
(11)
t
where: o(t), p(t + 1) – vector of individual reproduction
rates or soil degradation for state and control variables.
The multicriteria optimization model could be
solved with goal programming. Its creators are Charnes
and Cooper (Charnes and Cooper, 1961). This approach
incorporates solving a constructed model separately due
to each criterion. After acquiring optimal results, each
goal function is treated as the model’s separate limiting
condition in the form of:
m(t)T u(t) + w(t + 1)T x(t + 1) = dr
(12)
g(t)T u(t) + k(t + 1)T x(t + 1) = pr
(13)
o(t)T u(t) + p(t + 1)T x(t + 1) = so
(14)
where:
dr – the greatest agricultural income value acquired
from a single-criterion model’s solution
pr – optimal agricultural production volume acquired
from a single-criterion model’s solution
so – the amount of organic substance retained in the
soil, acquired from a single-criterion model’s optimal solution.
All of these conditions include a restrictive limitation
of equality type that should be weakened. A complete
equality’s weakening is called transformation which
includes variables of deficiency (u–) or excess (u+) expressing the achieved values’ non-fulfillment volume in
single-criterion models. After transformation, the added
limiting conditions will take on the form of:
m(t)T u(t) + w(t + 1)T x(t + 1) – u1+ + u2– = dr (15)
g(t)T u(t) + k(t + 1)T x(t + 1) – u3+ + u4– = pr
(16)
o(t) u(t) + p(t + 1) x(t + 1) – u + u6 = so
(17)
T
T
+
5
–
Next, many criteria are replaced with a single distance function describing the costs (penalties) of deviations from the target values. This functions includes
both variables regarding agricultural income and agricultural production deficiency or excess, as there are
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no specific recommendations on how to achieve them.
However, the soil organic substance deficiency must be
minimized so as not to degrade the natural environment.
The distance function will take on the form of:
F = u1+ + u2– + u3+ + u4– + u6– → min
(18)
FORMATION OF FARM MULTICRITERIA
MODELS
For two types of farms: field crops and mixed (plant
and animal production) in 2009–2012, linear dynamic
multicriteria optimization models have been created.
In 2012 the type of field crops covered 10% of farms
in Poland, and the type of mixed crops up 61%. They
focused the largest area of arable land in the country
(respectively 20.8% and 52.1%).
The first model dealing with the crop production
consisted of 44 decisive variables and 47 constraints.
Whereas, the second model constructed for an average
farm dealing with the crop and livestock production
comprised 104 variables and 122 balance constraints.
To build these models, data from Central Statistical Office (GUS) and Farm Accountancy Data Network
(FADN) were used. The collected information concerning area of agricultural land, area and structure of crops
and grants came from the publication FADN (Polski
FADN 2011–2014). While, the data about yields, prices
of agricultural products and means of production came
from CSO (GUS 2009–2012).
Tables 1 and 2 include selected information about
farm.
Average values from the acquired information were
or allowed for calculation of technical and economic parameters, free terms and objective function coefficients.
Limiting factors of models in each studied year formed
a linear programming model. Merging of these models occurred via crop and livestock herd rotation, that
is through dynamics equations also known as binding
conditions. Table 3 depicts the accepted crop rotation’s
scheme. In order to assure constant field area in rotation,
crop acreage was assumed to be average for the studied
years.
After rye and oats harvest in a farm dedicated solely to crop production, sowing of winter aftercrop was
planned. It consisted of mulch for sugar beet growing
and was a source of natural fertilizers.
435
Zaród, J. (2016). Sustainable development of the farms in Poland. J. Agribus. Rural Dev., 3(41), 433–443. DOI: 10.17306/JARD.2016.71
Table 1. Basic characteristics of a farm – type of field crops
Tabela 1. Podstawowe charakterystyki gospodarstwa rolnego – typ uprawy polowej
Specification – Wyszczególnienie
2009
2010
2011
2012
Agricultural land area (ha)
Powierzchnia użytków rolnych (ha)
53.0
66.5
64.5
64.4
Sown area (ha)
Powierzchnia zasiewów (ha)
52.07
65.49
63.69
63.55
Structure of sown area, of which (%):
Struktura zasiewów, w tym (%):
100
100
100
100
Cereals – Zbóż
66.89
63.16
66.12
67.6
Industrial crops – Roślin przemysłowych
21.0
25.03
23.06
21.4
Potatoes – Ziemniaków
4.98
5.2
4.13
4.53
Feed crops – Roślin pastewnych
4.75
4.23
4.49
4.44
Other crops – Innych upraw
2.38
2.38
2.2
2.03
Yields (dt·ha-1): – Plony (dt·ha-1):
Cereals – Zbóż
34.8
35.6
34.3
34.6
Rape – Rzepaku
30.8
23.6
22.4
26.4
Potatoes – Ziemniaków
Sugar beets – Buraków cukrowych
Procurement prices (PLN·dt ):
Ceny skupu (zł·dt-1):
191
211
232
242
543
483
574
582
48.26
59.84
81.99
88.68
32.74
41.12
74.24
74.40
Barley – Jęczmienia
40.8
48.98
75.38
81.49
Oats – Owsa
30.82
34.30
64.34
65.07
Triticale – Pszenżyta
37.05
46.65
72.01
79.56
Potatoes – Ziemniaków
31.73
36.53
37.0
37.76
Sugar beets – Buraków cukrowych
11.57
11.31
14.40
13.72
108.24
127.76
183.91
183.91
-1
Wheat – Pszenicy
Rye – Żyta
Rape – Rzepaku
Source: own elaboration based on data from GUS and FADN.
Źródło: opracowanie własne na podstawie danych GUS i FADN.
The numbers of cows and sows was determined
based on the average value in 2009–2012. However, the
numbers of the remaining animal species were derived
from the changeable composition of the livestock.
In order to reflect processes occurring in a farm as
accurately as possible, the models encompass a series of
balances ensuring inner consistency (balances of crop
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rotation, livestock herd rotation, mineral and natural fertilizing, animal nutrition, working hours).
In the crop production-only mode, the parameters
of first goal function individual agricultural income
were calculated as a difference between production
value (price × crop) and production cost (Augustyńska-Grzymek, 2012) without pricing the farmer’s work.
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Zaród, J. (2016). Sustainable development of the farms in Poland. J. Agribus. Rural Dev., 3(41), 433–443. DOI: 10.17306/JARD.2016.71
Table 2. Basic characteristics of a farm – type mixed*
Tabela 2. Podstawowe charakterystyki gospodarstwa rolnego – typ mieszany*
Specification – Wyszczególnienie
2009
2010
2011
2012
Agricultural land area (ha) – Powierzchnia użytków rolnych (ha)
29.9
29.1
28.7
29.8
Sown area (ha) – Powierzchnia zasiewów (ha)
29.55
28.52
28.16
29.17
Structure of sown area, of which (%): – Struktura zasiewów (%), w tym:
100
100
100
100
Cereals – Zbóż
65.04
63.85
63.88
64.9
Industrial crops – Roślin przemysłowych
22.1
23.0
23.06
23.4
7.0
6.5
Potatoes – Ziemniaków
6.03
5.53
Feed crops – Roślin pastewnych
6.19
5.1
5.42
5.11
Other crops – Innych upraw
0.64
1.05
1.14
1.06
Beef for slaughter (PLN·kg-1) – Żywca wołowego (zł·kg-1)
4.52
4.56
5.58
6.40
Pork for slaughter (PLN·kg-1) – Żywca wieprzowego (zł·kg-1)
4.56
3.89
4.52
5.45
Milk (PLN·l ) – Mleka (zł·l )
0.9
1.07
1.21
1.20
Procurement prices: – Ceny skupu:
-1
-1
Cattle (heads), of which (%): – Pogłowie bydła, w tym (%):
Cows – Krów
Pigs (heads), of which (%): – Pogłowie trzody chlewnej, w tym (%):
Sows – Loch
23
23
23
23
8
8
8
8
97
97
97
97
3
3
3
3
Yields and prices like in Table 1.
Source: own study based on data from GUS and FADN.
Plony i ceny upraw jak w tabeli 1.
Źródło: opracowanie własne na podstawie danych GUS i FADN.
Table 3. Crop succession covered by the models
Tabela 3. Następstwo roślin uwzględnione w modelach
Year – Rok
Field I – Pole I
Field II – Pole II
Field III – Pole III
Field IV – Pole IV
2008
Potatoes – Ziemniaki
Beats – Buraki
Oats – Owies
Wheat – Pszenica
Barley – Jęczmień
Rape – Rzepak
Triticale – Pszenżyto
Rye – Żyto
Other crops – Inne uprawy
2009
Wheat – Pszenica
Barley – Jęczmień
Rape – Rzepak
Triticale – Pszenżyto
Rye – Żyto
Other crops – Inne uprawy
Potatoes – Ziemniaki
Beats – Buraki
Oats – Owies
2010
Rape – Rzepak
Triticale – Pszenżyto
Rye – Żyto
Other crops – Inne uprawy
Potatoes –Ziemniaki
Beats – Buraki
Oats – Owies
Wheat – Pszenica
Barley – Jęczmień
2011
Rye – Żyto
Other crops – Inne uprawy
Potatoes – Ziemniaki
Beats – Buraki
Oats – Owies
Wheat – Pszenica
Barley – Jęczmień
Rape – Rzepak
Triticale – Pszenżyto
Source: own elaboration.
Źródło: opracowanie własne.
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437
Zaród, J. (2016). Sustainable development of the farms in Poland. J. Agribus. Rural Dev., 3(41), 433–443. DOI: 10.17306/JARD.2016.71
The acquired income was increased by direct grants
(Single Area Payments and compensatory payments)
and by sugar payment in case of sugar beet. In the livestock breeding model for variables related to commodity production, the way of individual income calculation
was not changed. However, crops intended for feeds
and livestock for raising in objective function were burdened with costs decreased by additional payments.
Individual crop yields are second goal criterion factors in a crop production-only model. In livestock production model, crop production is expressed in one kind
of units (dt·ha-1) and livestock production in another
(kg, l), that is why it was presented in terms of value in
the goal function.
For the purpose of determining the parameters of the
third objective function, the soil organic matter reproduction and degradation coefficients according to Eich
and Kindler (Fotyma and Mercik, 1992) were used.
Reproduction and degradation factors describe the
degree of soil depletion or enrichment in organic matter
(in t·ha-1) for cultivation of a given crop kind or utilization of organic fertilizers’ specific dose.
RESEARCH RESULTS
The model solution was two-staged. Table 4 presents the production structure acquired through solving
Table 4. Linear-dynamic optimization models’ solutions with field crops
Tabela 4. Rozwiązania liniowo-dynamicznych modeli optymalizacyjnych z produkcją roślinną
Variables – Zmienne
1
Single-criterion models – Modele jednokryterialne
model I
model II
model III
Multicriteria model
Model wielokryterialny
2
3
4
5
61.20
61.20
61.20
61.20
8.57
8.57
8.57
8.57
*
*
*
2009
Sown area (ha), of which:
Powierzchnia zasiewów (ha), w tym:
Wheat – Pszenicy
Barley – Jęczmienia
0.61
0.61
0.61
0.61
Rye – Żyta
6.12
6.12
29.38
6.12
23.26
0.00
0.00
23.26
Triticale – Pszenżyta
2.45
25.70
2.45
2.45
Rape – Rzepaku
6.73
6.73
6.73
6.73
Potatoes – Ziemniaków
3.06
3.06
9.18
3.06
Oats – Owsa
Sugar beets – Buraków
6.12
6.12
0.00
6.12
Other crops – Innych upraw
4.28
4.28
4.28
4.28
Aftercrop – Poplonu
6.12
6.12
29.38
19.38
61.20
61.20
61.20
61.20
7.34
7.34
7.34
7.34
2010
Sown area (ha), of which:
Powierzchnia zasiewów (ha), w tym:
Wheat – Pszenicy
Barley – Jęczmienia
25.10
1.84
1.84
25.10
Rye – Żyta
4.90
28.15
4.90
4.90
Oats – Owsa
0.00
0.00
23.26
0.00
Triticale – Pszenżyta
1.22
1.22
1.22
1.22
Rape – Rzepaku
7.96
7.96
7.96
7.96
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Zaród, J. (2016). Sustainable development of the farms in Poland. J. Agribus. Rural Dev., 3(41), 433–443. DOI: 10.17306/JARD.2016.71
Table 4 cont. – Tabela 4 cd.
1
Potatoes – Ziemniaków
2
3
4
5
3.06
3.06
10.40
3.06
Sugar beets – Buraków
7.34
7.34
0.00
7.34
Other crops – Innych upraw
4.28
4.28
4.28
4.28
Aftercrop – Poplonu
4.90
28.15
28.15
4.90
61.20
61.20
61.20
61.20
Wheat – Pszenicy
7.96
7.96
0.00
7.96
Barley – Jęczmienia
2.44
2.44
33.66
2.44
2011
Sown area (ha), of which:
Powierzchnia zasiewów (ha), w tym:
Rye – Żyta
4.90
4.90
4.90
4.90
Oats – Owsa
0.00
23.26
0.00
0.00
25.09
1.84
1.84
25.09
7.34
7.34
7.34
7.34
Triticale – Pszenżyta
Rape – Rzepaku
Potatoes – Ziemniaków
2.45
2.45
9.18
2.45
Sugar beets – Buraków
6.73
6.73
0.00
6.73
Other crops – Innych upraw
4.28
4.28
4.28
4.28
Aftercrop – Poplonu
4.90
4.90
4.90
4.90
61.20
61.20
61.20
61.20
8.57
8.57
0.00
8.57
2012
Sown area (ha), of which:
Powierzchnia zasiewów (ha), w tym:
Wheat – Pszenicy
Barley – Jęczmienia
Rye – Żyta
Oats – Owsa
0.61
23.87
9.18
0.61
28.76
5.51
5.51
28.76
0.00
0.00
0.00
0.00
Triticale – Pszenżyta
3.67
3.67
26.93
3.67
Rape – Rzepaku
6.73
6.73
6.73
6.73
Potatoes – Ziemniaków
3.06
3.06
3.06
3.06
Sugar beets – Buraków
6.12
6.12
6.12
6.12
Other crops – Innych upraw
3.67
3.67
3.67
3.67
Aftercrop – Poplonu
6.12
6.12
5.51
28.76
Agricultural income (PLN)
Dochód rolniczy (zł)
Agricultural production (PLN)
Produkcja rolnicza (zł)
Organic substance amount (t)
Substancja organiczna gleby (t)
617 059.46
596 715.18
26 287.57
703 189.87
26 266.64
703 161.49
2.39
1.77
* Optimization criteria (I – agricultural income, II – agricultural production, III – organic substance amount).
Source: own calculations on Matlab program.
* Kryteria optymalizacji (I – dochód z rolnictwa, II – produkcja rolnicza, III – wielkość substancji organicznej).
Źródło: obliczenia własne w programie Matlab.
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Zaród, J. (2016). Sustainable development of the farms in Poland. J. Agribus. Rural Dev., 3(41), 433–443. DOI: 10.17306/JARD.2016.71
single-criterion models (first stage) and a multicriteria
model with profile plant.
Acquired optimal solutions are in line with the rules
of sustainable agriculture at the farm level. They include
the accepted crop rotation (Table 3), acceptance good
soil coverage with crops and timely performance of agricultural treatments.
Agricultural income and production were slightly
decreased in multicriteria model solution, and the organic substance amount in soil decreased by 25.94%,
however its positive value is evidence of non-degradation of natural environment.
The optimal solution for a model of a crop and livestock production farm also allows for a positive balance
of organic substance amount in soil. Table 5 presents crop
production structure acquired through solving single-criterion models (first stage) and a multicriteria model.
The state of individual livestock species in optimal
solutions was conditioned by their profitability, cow
and sow quantity and livestock herd closed rotation.
This amount was unchanging in each analysed year and
amounted to: 8 cows, 7.84 calves, 6.27 young beef cattle, 1 replacement heifers and 1 culled cows, 3 sows, 48
piglets, 46.56 pigs for fattening, 0.75 replacement sows
and 0.75 culled sows. The model retained fractional
livestock numbers which demonstrate a given specimen
not having been on the farm the entire year or incomplete utilization of stations.
Table 5. Linear-dynamic optimal models’ solutions with livestock production
Tabela 5. Rozwiązania liniowo-dynamicznych modeli optymalizacyjnych z produkcją zwierzęcą
Variables – Zmienne
1
Single-criterion models – Modele jednokryterialne
model I
model II
model III
Multicriteria model
Model wielokryterialny
2
3
4
5
28.85
28.85
28.85
28.85
0.00
3.17
0.00
3.17
*
*
*
2009
Sown area (ha), of which:
Powierzchnia zasiewów (ha), w tym:
Wheat – Pszenicy
Barley – Jęczmienia
5.34
2.17
12.98
2.17
Rye – Żyta
4.33
4.33
4.33
4.33
Oats – Owsa
0.00
0.00
0.00
0.00
Triticale – Pszenżyta
9.09
9.09
1.44
9.09
Rape – Rzepaku
4.04
4.04
4.04
4.04
Potatoes – Ziemniaków
1.73
1.73
1.73
1.73
Sugar beets – Buraków
3.17
3.17
3.17
3.17
Other crops – Innych upraw
1.15
1.15
1.15
1.15
Grasslands – Łąk
3.90
3.90
5.00
5.00
Pastures – Pastwisk
2.41
2.41
3.00
3.00
28.85
28.85
28.85
28.85
Wheat – Pszenicy
0.00
3.46
0.00
3.46
Barley – Jęczmienia
4.90
1.44
4.90
1.44
11.97
11.97
4.33
11.97
Oats – Owsa
0.00
0.00
0.00
0.00
Triticale – Pszenżyta
1.59
1.59
9.23
1.59
Rape – Rzepaku
3.75
3.75
3.75
3.75
2010
Sown area (ha), of which:
Powierzchnia zasiewów (ha), w tym:
Rye – Żyta
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Zaród, J. (2016). Sustainable development of the farms in Poland. J. Agribus. Rural Dev., 3(41), 433–443. DOI: 10.17306/JARD.2016.71
Table 5 cont. – Tabela 5 cd.
1
Potatoes – Ziemniaków
Sugar beets – Buraków
Other crops – Innych upraw
Grasslands – Łąk
Pastures – Pastwisk
2
3
4
5
2.02
3.46
1.15
4.00
2.64
2.02
3.46
1.15
4.00
2.64
2.02
3.46
1.15
5.00
3.00
2.02
3.46
1.15
5.00
3.00
28.85
28.85
28.85
28.85
3.75
1.73
4.04
7.65
1.30
3.61
1.88
3.61
1.30
3.88
1.96
3.75
1.73
4.04
7.65
1.30
3.61
1.88
3.61
1.30
3.88
1.96
0.00
5.48
11.68
0.00
1.30
3.61
1.88
3.61
1.30
5.00
3.00
3.75
1.73
4.04
7.65
1.30
3.61
1.88
3.61
1.30
5.00
2.49
28.85
28.85
28.85
28.85
0.00
13.13
3.61
0.00
2.02
3.46
1.59
3.75
1.30
3.78
1.40
3.17
9.96
3.61
0.00
2.02
3.46
1.59
3.75
1.30
3.78
1.40
0.00
5.48
3.61
7.65
2.02
3.46
1.59
3.75
1.30
5.00
3.00
3.17
9.96
3.61
0.00
2.02
3.46
1.59
3.75
1.30
5.00
1.40
2011
Sown area (ha), of which:
Powierzchnia zasiewów (ha), w tym:
Wheat – Pszenicy
Barley – Jęczmienia
Rye – Żyta
Oats – Owsa
Triticale – Pszenżyta
Rape – Rzepaku
Potatoes – Ziemniaków
Sugar beets – Buraków
Other crops – Innych upraw
Grasslands – Łąk
Pastures – Pastwisk
2012
Sown area (ha), of which:
Powierzchnia zasiewów (ha), w tym:
Wheat – Pszenicy
Barley – Jęczmienia
Rye – Żyta
Oats – Owsa
Triticale – Pszenżyta
Rape – Rzepaku
Potatoes – Ziemniaków
Sugar beets – Buraków
Other crops – Innych upraw
Grasslands – Łąk
Pastures – Pastwisk
Agricultural income (PLN)
Dochód rolniczy (zł)
Agricultural production (PLN)
Produkcja rolnicza (zł)
Organic substance amount (t)
Substancja organiczna gleby (t)
436 516.58
415 826.92
597 766.43
587 766.43
4.50
2.51
* Optimization criteria (I – agricultural income, II – agricultural production, III – organic substance amount).
Source: own calculations on Matlab program.
* Kryteria optymalizacji (I – dochód z rolnictwa, II – produkcja rolnicza, III – wielkość substancji organicznej).
Źródło: obliczenia własne w programie Matlab.
www.jard.edu.pl
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Zaród, J. (2016). Sustainable development of the farms in Poland. J. Agribus. Rural Dev., 3(41), 433–443. DOI: 10.17306/JARD.2016.71
The optimal solutions also included information on
crops sale (exceeding nutritional needs of livestock),
production materials’ purchase (mineral fertilizers and
concentrated mixtures) and the need for manpower.
Soil was enriched with organic substances with an
average of 0.091 t per one hectare in a crop and livestock production farm, and with 0.03 t in a crop-only
production farm.
The optimal solution of a crop and livestock production farm model yielded in the studied years (per one
hectare): agricultural income higher by 47.83%, production value higher by 77.32% and nearly three times as
much organic substances in soil than in a crop production-only farm.
CONCLUSIONS
The analysis subject was two types of Polish farms
(field crops and mixed) in 2009–2012. For these types
of farms, two linear-dynamic multicriteria optimization
models were created. The first model referred only to
crop production, the second one to livestock breeding
(cattle and pigs) and crop cultivation. Crop rotation has
significant influence on the production structure acquired in optimal solutions. The same crop succession
was used in both models, which allowed for a comparison of results. The accepted crop rotation allowed for
timely performance of agricultural treatments and good
soil coverage with crops. Crop cultivation was performed in line with the rules of integrated pest management (Dz. U. L 309 of 24.11.2009, p. 71–86, Art. 14 and
annex III). In a farm with livestock production, crops
gained their nutrients necessary for development mainly
from manure which was allocated for cultivation of potatoes sugar beet and oilseed rape (in respective doses
of: 300, 350 and 200 dt·ha-1). In a farm with no livestock, organic fertilization included straw and stubble
aftercrop plowing. The crops acquired in this manner
are characterized by high quality.
In both models, abiding to the rules of sustainable
production allowed for acquiring a positive balance of
organic substance in soil (1,77 t and 2,51 t).
The agricultural income in a crop production-only
farm, acquired in a multicriteria model’s optimal solution amounted to an annual average of 149 178.40
PLN (70 257.61/4) which is 12 431.57 PLN per
month for 3 persons. In livestock production farms,
442
agricultural income acquired in an optimal solution
amounted (587 766,43/(4×12) = 12 245.13 PLN).
Average monthly net salary (measure comparable to
agricultural income) in Polish enterprise sector, according to GUS (Zgierska, 2012) in 2012 was 2563.35 PLN.
Thanks to a large area of agricultural land and livestock
in the surveyed farms, the agricultural income was obtained higher than net wages in the corporate sector.
Nonetheless, the number of Polish farmers dealing
with livestock production is getting lower year by year.
The main reasons for this abandonment of livestock production are the high quality standards and no funds for
farm modernization.
The linear-dynamic multicriteria optimization models allow for checking the realization of economic,
production and ecologic goals for a period of a few
years in a farm upholding the sustainable development
principles.
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ZRÓWNOWAŻONY ROZWÓJ GOSPODARSTWA ROLNEGO W POLSCE
Streszczenie. Na podstawie danych GUS o gospodarstwach rolnych w Polsce zbudowano dwa liniowo-dynamiczne wielokryterialne modele optymalizacyjne. Pierwszy model dotyczył produkcji roślinnej, drugi produkcji roślinnej i zwierzęcej. W obu
modelach funkcje celu maksymalizowały dochód rolniczy i produkcję rolniczą oraz minimalizowały straty materii organicznej
w glebie. Równowaga pomiędzy tymi trzema celami jest istotą zrównoważonego rozwoju gospodarstwa rolnego. Modele rozwiązano za pomocą programowania celowego. W wyniku rozwiązania optymalnego otrzymano taką strukturę produkcji, która
daje najwyższy w danych warunkach dochód rolniczy, produkcję o wysokiej jakości i nie degraduje środowiska naturalnego.
Celem tego artykułu jest sprawdzenie możliwości zrealizowania równocześnie celu produkcyjnego, ekonomicznego i ekologicznego w przeciętnym gospodarstwie rolnym w Polsce na przestrzeni czterech lat.
Słowa kluczowe: zrównoważony rozwój, produkcja rolnicza, dochód rolniczy, materia organiczna gleby, wielokryterialny model optymalizacyjny
Accepted for print – Zaaakceptowano do druku: 11.07.2016
www.jard.edu.pl
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